---
title: "GaussianNB in JavaScript with @kanaries/ml"
description: "Classify continuous numeric features with the GaussianNB JavaScript and TypeScript implementation in @kanaries/ml for browser and Node.js applications."
canonical_url: "https://ml.kanaries.net/docs/apis/bayes/gaussianNB"
markdown_url: "https://ml.kanaries.net/docs/apis/bayes/gaussianNB.md"
---
# GaussianNB in JavaScript

## Algorithm overview

GaussianNB is a naive Bayes classifier for continuous numeric features. It models each feature per class with a Gaussian distribution and predicts the class with the highest posterior score.

## JavaScript implementation

`@kanaries/ml` exposes `Bayes.GaussianNB` for lightweight probabilistic classification in browser or Node.js applications.

## Quick start example

```ts
import { Bayes } from '@kanaries/ml';

const clf = new Bayes.GaussianNB({ varSmoothing: 1e-9 });
clf.fit([[1.0, 2.0], [1.2, 1.9], [4.0, 4.2], [4.2, 4.0]], [0, 0, 1, 1]);
const pred = clf.predict([[1.1, 2.1], [4.1, 4.1]]);
console.log(pred);
```

## Detailed API reference

```ts
new Bayes.GaussianNB(props?: {
  priors?: number[] | null;
  varSmoothing?: number;
})
```

Methods:

- `fit(X: number[][], y: number[]): void`
- `predict(X: number[][]): number[]`

`priors`, when provided, must match the number of fitted classes.